The Truth About AI and Job Loss

I keep finding myself in conversations with family and friends asking, “Is AI coming for our jobs?” Which roles are getting Thanos-snapped first? And will there still be space for junior individual contributors in organizations? And many more. 

With so many conflicting opinions, I felt overwhelmed and anxious, so I decided to take action instead of staying stuck in uncertainty. So, I began collecting historical data and relevant facts to gain a clearer understanding of the direction and impact of the current AI surge.

So, Here’s What We Know

1. Microsoft reports that over 30% of the code on GitHub Copilot is now AI-generated, highlighting a shift in how software is being developed. 
2. Major tech companies — including Google, Meta, Amazon, and Microsoft — have implemented widespread layoffs over the past 18–24 months. 
3. Current generative AI models, like GPT-4 and CodeWhisperer, can reliably write functional code, particularly for standard, well-defined tasks.
4. Productivity gains: Occupations in which many tasks can be performed by AI are experiencing nearly five times higher growth in productivity than the sectors with the least AI adoption.
5. AI systems still require a human “prompt” or input to initiate the thinking process. They do not ideate independently or possess genuine creativity — they follow patterns and statistical reasoning based on training data.
6. Despite rapid progress, today’s AI is still far from achieving human-level general intelligence (AGI). It lacks contextual awareness, emotional understanding, and the ability to reason abstractly across domains without guidance or structured input.
7. Job displacement and creation: The World Economic Forum's Future of Jobs Report 2025 reveals that 40% of employers expect to reduce their workforce where AI can automate tasks.
8. And many more.

There’s a lot of conflicting information out there, making it difficult to form a clear picture. With so many differing opinions, it's important to ground the discussion in facts. So, let’s break it down from a data engineer’s point of view — by examining the available data, identifying patterns, and drawing insights that can help us make sense of it all.

Navigating the Noise

Let’s start with the topic that’s on everyone’s mind — layoffs. It’s the most talked-about and often the most concerning aspect of the current tech landscape. Below is a trend analysis based on layoff data collected across the tech industry.

Although the first AI research boom began in the 1980s, the current AI surge started in the late 2010s and gained significant momentum in late 2022 with the public release of OpenAI's ChatGPT. The COVID-19 pandemic further complicated the technological landscape. Initially, there was a hiring surge to meet the demands of a rapidly digitizing world. 

However, by 2023, the tech industry experienced significant layoffs, with over 200,000 jobs eliminated in the first quarter alone. This shift was attributed to factors such as economic downturns, reduced consumer demand, and the integration of AI technologies.

Since then, as shown in Figure 1, layoffs have continued intermittently, driven by various factors including performance evaluations, budget constraints, and strategic restructuring. For instance, in 2025, companies like Microsoft announced plans to lay off up to 6,800 employees, accounting for less than 3% of its global workforce, as part of an initiative to streamline operations and reduce managerial layers.

Between 2024 and early 2025, the tech industry experienced significant workforce reductions. In 2024 alone, approximately 150,000 tech employees were laid off across more than 525 companies, according to data from the US Bureau of Labor Statistics. The trend has continued into 2025, with over 22,000 layoffs reported so far this year, including a striking 16,084 job cuts in February alone, highlighting the ongoing volatility in the sector.

It really makes me think — have all these layoffs contributed to the rise in the US unemployment rate? And has the number of job openings dropped too? I think it’s worth taking a closer look at these trends.

Figure 2 illustrates employment and unemployment trends across all industries in the United States. Interestingly, the data appear relatively stable over the past few years, which raises some important questions. If layoffs are increasing, where are those workers going? And what about recent graduates who are still struggling to land their first jobs?

We’ve talked about the layoffs — now let’s explore where those affected are actually going. While this may not reflect every individual experience, here’s what the available online data reveals.

After the Cuts

Well, I wondered if the tech job openings have decreased as well? 

Even with all the news about layoffs, the tech job market isn’t exactly drying up. 

As of May 2025, there are still around 238,000 open tech positions across startups, unicorns, and big-name public companies. Just back in December 2024, more than 165,000 new tech roles were posted, bringing the total to over 434,000 active listings that month alone. And if we look at the bigger picture, the US Bureau of Labor Statistics expects an average of about 356,700 tech job openings each year from now through 2033. A lot of that is due to growth in the industry and the need to replace people leaving the workforce. So yes — while things are shifting, there’s still a strong demand for tech talent, especially for those keeping up with evolving skills.

With so many open positions still out there, what’s causing the disconnect when it comes to actually finding a job?

New Wardrobe for Tech Companies

If those jobs are still out there, then it’s worth digging into the specific skills companies are actually hiring for.

Recent data from LinkedIn reveals that job skill requirements have shifted by approximately 25% since 2015, and this pace of change is accelerating, with that number expected to double by 2027. In other words, companies are now looking for a broader and more updated set of skills than what may have worked for us over the past decade. 

The graph indicates that technical skills remain a top priority, with 59% of job postings emphasizing their importance. In contrast, soft skills appear to be a lower priority, mentioned in only 46% of listings, suggesting that companies are still placing greater value on technical expertise in their hiring criteria.

Focusing specifically on the comparison between all tech jobs and those requiring AI skills, a clear trend emerges. As of 2025, around 19% to 25% of tech job postings now explicitly call for AI-related expertise — a noticeable jump from just a few years ago. This sharp rise reflects how deeply AI is becoming embedded across industries. In fact, nearly one in four new tech roles now list AI skills as a core requirement, more than doubling since 2022. 

Python remains the most sought-after programming language in AI job postings, maintaining its top position from previous years. Additionally, skills in computer science, data analysis, and cloud platforms like Amazon Web Services have seen significant increases in demand. For instance, mentions of Amazon Web Services in job postings have surged by over 1,778% compared to data from 2012 to 2014 

While the overall percentage of AI-specific job postings is still a small fraction of the total, the upward trend underscores the growing importance of AI proficiency in the modern workforce.

Final Thought

I recognize that this analysis is largely centered on the tech industry, and the impact of AI can look very different across other sectors. That said, I’d like to leave you with one final thought: technology will always evolve, and the real challenge is how quickly we can evolve with it before it starts to leave us behind.

We’ve seen this play out before. In the early 2000s, when data volumes were manageable, we relied on database developers. But with the rise of IoT, the scale and complexity of data exploded, and we shifted toward data warehouse developers, skilled in tools like Hadoop and Spark. Fast forward to the 2010s and beyond, we’ve entered the era of AI and data engineers — those who can manage the scale, variety, and velocity of data that modern systems demand.

We’ve adapted before — and we’ve done it well. But what makes this AI wave different is the pace. This time, we need to adapt faster than we ever have in the past.